Performance Of Optimization Process Research Articles

The development of a generalized riser flow regime map based upon higher moment and chaotic statistics using electrical capacitance volume tomography (ECVT)

Dynamic analyses have been applied to the temporal signals from an Electro Capacitance Volume Tomography instrument located near mid-height on the riser of an industrial-scale cold-flow circulating fluidized bed to characterize gas-solids flow behavior in the riser. Twelve capacitance electrodes surround the cylindrical riser over a height of 1.3 m. The instrument used a neural network deconvolution algorithm to determine the spatially resolved solids fraction recorded at 52 Hz. Experiments were carried out over a range of gas and solids flows in the transport regime using a Geldart Group B bed material, high density polyethylene with mean particle size of 880 μm. The radial solids distribution was found to vary from one-time step to the next between profiles typical of laminar and turbulent flow. The duration of time spent in each of these flow profiles depended upon the operating regime – dilute, core-annular, or fast fluidized bed. The chaotic structure of the temporal data was characterized using the three conventional approaches: the first 4 moments from the distribution of signal in time, system memory parameters from the autocorrelation function and the Hurst exponent, and analysis of the correlation entropy and correlation dimension of the attractor. These signal analysis techniques were used to clearly distinguish differences between different transport operating regimes. Specifically, it was experimentally observed that a riser transitions from core annular flow profile to dilute and dense regimes via increasing the frequency of short term transients to either dilute or dense flow profiles, respectively. A regime map was generated based upon these dynamics using solids flux and gas velocity axes. Fast fluidized, core annular, and dilute each exhibited different degree of dynamic characteristics typical of fluid dominated or particle compromising behavior. It should be noted that the magnitude for the different statistics? was in the same range regardless of the regime, it was the radial profile for the statistic that changed and subsequently identified that there was a change in the regime. Finally, a reduced regime map was developed consisting of plotting the gas velocity normalized by the upper transport velocity versus the solids flux normalized by the saturation carrying capacity. The use of this reduced plot allowed the data from widely different conditions to be plotted and compared on the same graph. Note that in many instances, some of the statistics identified the operating point as being in one regime while others indicated that it was in another indicating a transition region between dilute or core annular regimes and between the core annular and fast fluidization regimes. This now provides a tool that can be used to optimize process performance, identify changes in operating states, or replicate process dynamics during process scaling or changing operating parameters.

Experimental characterization of a solar powered MSF desalination process performance

Desalination technology has become a significant solution of fresh drinking water for many parts of the world. Lack of fresh water resources in dry environments has encouraged the establishment of desalination processes and developed technology to compensate for water scarcity. However, conventional desalination plants rely mainly on using fossil fuels for operating the process, rendering desalination costly and augmenting environmental pollution. For such difficulties, using renewable energy for desalination plants is a beneficial alternative in terms of economical, environmental and sustainability aspects. In this research work, flat plate solar collectors were integrated with multistage vacuum chambers and operated a solar multi stage flash (MSF) desalination facility. An experimental parametric study has been carried out that leads to optimum process performance and cost effective desalination technology. A major finding of this research was that by reducing 20% of atmospheric pressure in a vacuum flash chamber, the distillation to evaporation ratio was enhanced by 53% and specific energy consumption was reduced by 35%. The best distillation to evaporation ratio was 0.42 at a feed flow of 0.5 L/min.

Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance

Thus far, the Taguchi technique is found only efficient in obtaining the combination of optimal factor settings when a single product/process response is considered. In today’s dynamic environment, customers are interested in multiple quality responses. This research, therefore, utilizes fuzzy logic and backward-propagation neural networks (BPNNs) to optimize process performance for products of multiple quality responses. In this research, quality characteristics are transformed to signal to noise (S/N) ratios, which are then used as inputs to a fuzzy model to obtain a single common output measure (COM). Next, BPNNs are employed to obtain full-factorial experimental data. Finally, the combination of factor levels that maximizes the average COM value is chosen as the optimal combination. Three case studies are provided for illustration; in all of which the proposed approach provided the largest total anticipated improvement. This indicates that the proposed approach is more efficient than Taguchi-fuzzy, grey-Taguchi, and Taguchi-utility methods. In conclusion, the fuzzy-BPNN approach may greatly assist process/product engineers in optimizing performance with multiple responses in a wide range of business applications.

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people’s requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

Production and characterization of brake pad developed from coconut shell reinforcement material using central composite design

In this work, locally sourced non-hazardous materials were used to produce brake pad using grey relational analysis (GRA) and experimental design via central composite design. Raw materials selected for production include coconut shell, epoxy resin (binder), graphite (friction modifier) and aluminum oxide (abrasive). Twenty-seven samples were produced separately using coconut shell as reinforcement material by varying process parameters. Formulation of the brake pads samples was done using rule of mixture and a weight percent of 52% reinforcement material, 35% binder, 8% abrasive and 5% friction modifier were used for the production. Grey relational analysis (GRA) shows that optimal process performance can be obtained using molding pressure, molding temperature, curing time and heat treatment time of 14 MPa, 140 °C, 8 min and 5 h, respectively. Optimized sample was produced using the optimal set of process parameters obtained from GRA and compared with commercially available sample produced by Ibeto Group. The experimental results showed that the performance of the optimized coconut shell-reinforced brake pad compared satisfactorily with commercially available samples and capable of producing less brake noise and vibration during application. Analysis of variance shows that curing time with a contribution of 30.38% and 31.40% have the most significant effect on the hardness and ultimate tensile strength of the coconut shell-reinforced friction material, respectively, while heat treatment time with a contribution of 46.3% and 24.23% have the most significant effect on the wear rate and friction coefficient of coconut shell-reinforced brake pad, respectively. The effects of all the factors on the properties of the friction materials are significant since their p values are greater than 0.010 (1%).

Negative Total Float to Improve a Multi-Objective Integer Non-Linear Programming for Project Scheduling Compression

This paper presents Multi-Objective Integer Non-Linear Programming (MOINLP) involving Negative Total Float (NTF) for improving the basic model of Multi-Objective Programming (MOP) in case the optimization of the additional cost for Project Scheduling Compression (PSC). Using the basic MOP to solve the more complex problems is a challenging task. We suspect that Negative Total Float (NTF) having an indication to make the basic MOP to solve the more general case, both simple and complex of PSC. The purpose of this research is identifying the conflicting objectives in PSC problem using NTF and improving MOINLP by involving the NTF parameter to solve the PSC problem. The Solver Application, which is an add-in of MS Excel, is used to perform optimization process to the model developed. The results show that NTF has an important role to identify the conflicting objectives in PSC. We define NTF is an automatic maximum value of the activity duration reduction to achieve due date of PSC. Furthermore, the use of NTF as a constraint in MOINLP can solve the more general case for both simple and complex PSC problem. Base on the condition, we state that the basic MOP is still significant to solve the PSC complex problems using MOINLP as a sophisticated MOP technique.

Approach for Multicriteria Equipment Redesign in Sterile Manufacturing of Biopharmaceuticals

There is a continuous search for efficient optimization methods in the production of biopharmaceuticals, with a special focus on reducing production time and costs. Pharmaceutical production is currently dominated by batch production processes that have a relatively long downtime for cleaning and reconfiguration of the plant equipment when the product or its format size is changed. The geometry and design of the equipment can strongly affect process performance. This work presents an approach for generating and assessing design alternatives for process equipment used in the sterile filling of biopharmaceutical drug products. A case study is presented based on background information provided by Hoffmann-La Roche for a surge tank used in the filling of liquid vials. Three criteria—namely equipment cleanability, production efficiency, and flexibility—are used to evaluate the proposed shape alternatives for the surge tank. Computational fluid dynamics (CFD) was applied to investigate the flow behavior of cleaning water inside the tank. A trade-off in the results was observed between the different evaluation criteria for the investigated basic shapes. However, it was found that a different part of the tank controls the results obtained for each criterion. An alternative tank design is presented that combines the optimal features to achieve the best possible performance for all assessment criteria. The transfer and application of the established CFD technique to the field of sterile manufacturing of biopharmaceuticals were demonstrated and facilitated by the introduction of appropriate model simplifications. Valuable insights on equipment cleanability and on the measures required to achieve cleanliness were obtained, which proves the importance of the application of such techniques. Redesigning equipment and applying geometrical changes were shown to be effective measures for process performance optimization.

Process performance optimization and mathematical modelling of a SBR-MBBR treatment at low oxygen concentration

Abstract In this study, performances of a SBR-MBBR in terms of COD, TN removal efficiency and sludge production were compared to that of an A/O-MBBR. The SBR-MBBR (40% filling ratio) at 24, 18 and 12 h HRT had a COD and TN removal efficiencies of 88 and 75%, 92 and 72%, 90 and 47%. Increasing the filling ratio to 60% with 12 h HRT, the COD and TN removal efficiencies raised to 93% and 66%, respectively. Thus, increasing the filling ratio of the MBBR the TN removal was improved. The A/O-MBBR configuration achieved a COD and TN removal efficiencies, respectively, of 85% and 72%. This configuration obtained the highest sludge yield due to an increase of the sludge production. Nitrification and denitrification activity tests, performed on attached and suspended biomass, revealed a specialization of the microbial community with the suspended biomass responsible for the denitrification process while the attached biomass for the nitrification. Moreover, a mathematical model consisting of a system of impulsive ordinary differential equations was developed to simulate the SBR-MBBR process. The mathematical model was successfully calibrated and validated through the collected experimental data, resulting in a suitable tool for process efficiency prediction and optimization of operational process conditions.

An efficient polynomial chaos expansion strategy for active fault identification of chemical processes

This paper is concerned with a highly efficient active fault detection and isolation (FDI) framework. An auxiliary, fault-revealing input is derived by solving an optimization problem. As we implement a model-based approach, the active FDI framework is robustified against model parameter uncertainties, including parameter correlations which are common for experimentally derived parameters. Moreover, critical safety limits are considered, and an optimal process performance is fulfilled in parallel. In this work, which is an extension to our ESCAPE-2017 contribution, a novel highly effective polynomial chaos expansion (PCE) approach is used to address parameter uncertainties and to include process design parameters directly. To reduce the computational load, we combined the PCE with a least angle regression (LAR) strategy. The overall effectiveness of the novel one-shot sparse polynomial chaos expansion (OS2-PCE) concept is demonstrated by analyzing a tubular plug flow reactor illustrating the need for uncertainty and parameter correlation analysis in FDI while ensuring an optimal and safe process operation, respectively.

Wettability in complex porous materials, the mixed-wet state, and its relationship to surface roughness

A quantitative in situ characterization of the impact of surface roughness on wettability in porous media is currently lacking. We use reservoir condition micrometer-resolution X-ray tomography combined with automated methods for the measurement of contact angle, interfacial curvature, and surface roughness to examine fluid/fluid and fluid/solid interfaces inside a porous material. We study oil and water in the pore space of limestone from a giant producing oilfield, acquiring millions of measurements of curvature and contact angle on three millimeter-sized samples. We identify a distinct wetting state with a broad distribution of contact angle at the submillimeter scale with a mix of water-wet and water-repellent regions. Importantly, this state allows both fluid phases to flow simultaneously over a wide range of saturation. We establish that, in media that are largely water wet, the interfacial curvature does not depend on solid surface roughness, quantified as the local deviation from a plane. However, where there has been a significant wettability alteration, rougher surfaces are associated with lower contact angles and higher interfacial curvature. The variation of both contact angle and interfacial curvature increases with the local degree of roughness. We hypothesize that this mixed wettability may also be seen in biological systems to facilitate the simultaneous flow of water and gases; furthermore, wettability-altering agents could be used in both geological systems and material science to design a mixed-wetting state with optimal process performance.

Energy efficiency for condition-based maintenance decision-making: Application to a manufacturing platform

Condition-based maintenance has been developed and successfully applied in various industrial systems to preventively maintain the correct equipment at the right time with regard to its current health “condition” such as oil temperature, harmonics data, and vibration. The monitoring of these conventional indicators may however be costly. Moreover, energy efficiency addressed by sustainability requirements has been recently considered as an emerging key performance indicator to be controlled. Nevertheless, this emerging key performance indicator is not yet integrated in condition-based maintenance decision-making. To face these issues, the main objective of this article is to investigate the interests to use energy efficiency for condition-based maintenance decision-making. The first original contribution of this article is to propose a new energy efficiency–based condition-based maintenance model using energy efficiency indicator which is defined as the amount of energy consumption to produce one useful output unit. The proposed model leads to consider not only the maintenance cost but also energy and useful output performance in the condition-based maintenance optimization process. The second contribution concerns an investigation of the proposed energy efficiency–based condition-based maintenance model for the case study of the TELMA platform. The performance of the proposed model is verified by comparing to an extended traditional one. The obtained results allow to highlight the impacts of energy efficiency on existing condition-based maintenance strategies and to conclude on the interest of a new energy efficiency indicator–based condition-based maintenance practice in terms of both cost and efficiency.

A fuzzy goal programming-regression approach to optimize process performance of multiple responses under uncertainty

ABSTRACTThis paper proposes an approach to optimize performance of manufacturing processes for multiple quality responses using a fuzzy goal programming-regression approach. Firstly, a multiple nonlinear regression model is formulated for each response replicate and then utilized in fuzzy goal programing to obtain the combinations of optimal factor levels. Fuzzy regression model is then developed for each quality response. In order to determine the combination of optimal factor settings for multiple responses, the desirability functions and pay-off matrices are constructed and then employed to formulate the lower, middle, and upper optimization models. Three case studies, which were investigated in previous literature, are employed for illustration. Compared to the Taguchi method, artificial neural networks, fuzzy regression, and grey-Taguchi method, the proposed approach provides larger anticipated improvement in quality responses, efficiently deals with fuzziness and irregular process performance, and effectively considers preferences on process settings and response values in all the three case studies. Finally, a confirmation experiments in plastic pipes industry to validate the obtained results. In conclusion, the proposed approach may provide valuable support to process engineers in optimizing process performance of multiple quality responses under uncertainty in a wide range of industrial applications.

Multi-objective versus single-objective optimization of batch bioethanol production based on a time-dependent fermentation model

Traditional process simulators, such as Aspen Plus, are inadequate for optimizing multiple-objective systems in fermentation-based processes. This work uses a novel integrated platform of the robust genetic algorithm optimization in MATLAB linked with an Aspen Plus unsteady-state batch fermentation simulation to optimize the batch ethanolic fermentation process with respect to initial substrate concentration, fermentation time, and in situ product removal. A time-dependent fermentation model that utilizes both glucose and xylose, the major sugars present in lignocellulosic hydrolysate, with Monod cell growth kinetics, substrate and product inhibitions, is used as a model system. The optimized design variables from the multi-objective optimization (MOO) and single-objective optimization (SOO) suggest the typical concentrations of sugars from lignocellulosic hydrolysate must be concentrated to optimize the performance of the batch fermentation process. Furthermore, time-dependent information from an unsteady-state simulation was used to design an integrated batch fermentation with in situ product recovery, allowing higher initial sugars concentrations to be used in the fermentation process (about 50%, for the best optimal solution in the MOO). This resulted in 15% ethanol productivity, 143% total ethanol produced, and 67% fraction of sugar converted improvements relative to the batch fermentation without product recovery. Unlike the single optimal solution from the SOO, MOO presents many equally optimal solutions that can be used as a decision-support tool to guide the choice of design variables for optimum process performance. This study creates a platform that can be used to optimize integrated biorefinery and refinery processes.

Optimization of Multiproduct Biorefinery Processes under Consideration of Biomass Supply Chain Management and Market Developments

Even though a shift from conventional to renewable resources is often envisaged, lignocellulosic biorefinery concepts struggle with economic viability and sustainability. In order to overcome these barriers, a full analysis from biomass supply chain, process performance optimization, and product-portfolio selection is targeted. Addressing the economic viability and sustainability already at an early process development stage when only limited knowledge is available, Process Network Flux Analysis (PNFA) [Ulonska et al., AIChE J. 2017, 62, 3096–3108] is capable of systematically identifying the most valuable processing pathways. This enables a first performance ranking based on the profit or global warming potential of pathways, thereby accelerating process development. While so far only processing networks have been considered, the methodology is herein extended to consider biomass supply chain optimization and market-dependent price developments such that all main influencing factors are considered simult...

A novel temperature swing adsorption process for natural gas purification: Part I, model development

Abstract This article investigates a novel TSA-based gas separation cycle that uses a microchannel monolith with a hollow polymer-adsorbent matrix coated along the inner walls of each microchannel. CO2 is removed from CH4 by passing impure feed gas through the microchannels, followed by a sequential flow of desorbing hot liquid, cooling liquid, and purge gas through the same microchannels. This configuration is expected to augment the heat and mass transfer to the adsorbent due to direct contact between the transfer fluid and the adsorbent layer, and also reduce the overall system size due to flow of the working and coupling fluids through the same microchannels. In this Part I of a two-part study, computational models are developed to study the relevant fluid dynamics, heat transfer, and mass transfer in each process. Parametric studies are conducted to determine the optimum microchannel geometry and adsorbent and heat transfer fluid (HTF) materials. The process is expected to purify up to two orders of magnitude greater gas throughput compared to bed-based designs. In the accompanying paper, Part II, a comprehensive process performance map and optimization of energy requirements for the process are discussed.

Design of alternate solvent for recovery of residual palm oil: simultaneous optimization of process performance with environmental, health and safety aspects

In this work, a computer-aided molecular design (CAMD)-based approach has been developed to design alternative solvents for extracting palm oil from palm pressed fibre (PPF). PPF is a by-product from palm oil milling process produced during the extraction of crude palm oil from fresh fruit bunches. It is noted that the oil that found in PPF consists of high concentration of carotenoids; therefore, palm oil industries are trying to recover the residual oil in PPF. Hexane is conventionally used as a solvent to extract PPF. However, hexane has high boiling point which leads to significant degradation of carotene during solvent recovery. Besides, hexane is considered as a toxic chemical; thus, the extracted oil cannot be used in food applications. In order to overcome the limitations of hexane, this work presents a systematic approach to generate alternative solvents with desirable attributes for PPF oil extraction via CAMD with the integration of safety and health aspects. In this work, diffusivity is defined as a key parameter to evaluate the solvent functionality because the solvent must diffuse through the fibrous matrix to solubilize trapped oil. The safety and health aspects were assessed by implementing disjunctive programming to discretize the properties for allocation of subindex scores. The developed algorithm can trade-off the process and environmental, health and safety (EHS) performances and identify alternative solvents which improve EHS performance compare with hexane.

Ligand-Mediated Nanocrystal Growth.

A microfluidic platform combined with a deterministic model accounting for surface ligands reveals precious insights into the nanocrystal formation process. The comparison of on-line kinetic information with model predictions enables the derivation of temperature-dependent kinetic parameters for the CdSe model system. This fully generalizable approach represents a step forward toward a quantitative prediction of the nanocrystal size distribution, enabling the control and optimization of process performance and material properties.

Dual robust control of batch processes based on optimality-conditions parameterization

This paper presents a scheme for dual robust control of batch processes under parametric uncertainty. Some recently proposed approaches can be used to tackle this problem, however, this will be done at the price of conservativeness or significant computational burden. In order to increase computational efficiency, we propose a scheme that uses parametrized conditions of optimality in the adaptive predictive-control fashion. The dual features of the controller, i.e., balance between the control moves that excite the system to improve accuracy of the parameter estimation and between the moves that optimize process performance, is realized through scenario-based (multi-stage) approach, which allows for modeling of the adaptive robust decision problem and for projecting this decision into predictions of the controller. The proposed approach is illustrated on a case study from batch membrane filtration.

A Smart Collaborative Charging Algorithm for Mobile Power Distribution in 5G Networks

The issues of energy supply had been widely investigated to upgrade the network lifetime, power utilization, and system stability of communication networks. However, the deployable charging solution for massive mobile terminals in 5G is still lack. Although prior studies in wireless rechargeable sensor networks focused on the energy usage effectiveness, the charging time was frequently overlooked in most schemes. In this paper, we propose a novel distributed mobile charging (DMC) algorithm to optimize the charging time and charger quantity. The traditional policies are first analyzed based on the energy source. We noticed that the location of base station is extremely significant for charging performance. Then, the details of the DMC algorithm are illustrated through the main process of energy transfer, formula deductions, and performance optimization. To further promote the network capacity, an improved algorithm named adaptive dynamic energy transfer is proposed by introducing linear node sleeping mechanism. The simulation results demonstrate that the algorithms are able to improve the charging time and charger quantity in multiple scenarios.

Bioethanol Production via Syngas Fermentation

Bioconversion of C-1 carbon in syngas through microbial fermentation presents a huge potential to be further explored for ethanol production. Syngas can be obtained from the gasification of lignocellulosic biomass, by which most of carbon content of the biomass was converted into CO and CO2. These gases could be further utilized by carbon-fixing microorganism such as Clostridium sp. to produce ethanol as the end product. In order to obtain an optimum process, a robust and high performance strain is required and thus high ethanol yield as the main product can be expected. In this study, series of batch fermentation was carried out to select high performance strains for ethanol production. Bottle serum fermentations were performed using CO-gas as the sole carbon source to evaluate the potential of some Clostridia species such as Clostridium ljungdahlii, C. ragsdalei, and C. carboxidovorans in producing ethanol at various concentration of yeast extract as the organic nitrogen source, salt concentration, and buffer composition. Strain with the highest ethanol production in the optimum media will be further utilized in the upscale fermentation.